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Structural Equation Modeling: A Second Course (Quantitative Methods in Education and the Behavioral Sciences) | 
enlarge | Creators: Gregory R. Hancock, Ralph O. Mueller Publisher: IAP - Information Age Publishing Inc. Category: Book
Buy New: $39.99
New (13) Used (3) from $39.99
Rating: 2 reviews Sales Rank: 237887
Media: Paperback Pages: 448 Number Of Items: 1 Shipping Weight (lbs): 1.3 Dimensions (in): 9.1 x 6.1 x 1
ISBN: 1593110146 Dewey Decimal Number: 519.53 EAN: 9781593110147
Publication Date: January 30, 2006 Shipping: Eligible for Super Saver Shipping Availability: Usually ships in 24 hours
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| Editorial Reviews:
Product Description A volume in Quantitative Methods in Education and the Behavioral Sciences: Issues, Research, and Teaching (sponsored by the American Educational Research Association s Special Interest Group: Educational Statisticians) Series Editor Ronald C. Serlin, University of Wisconsin-Madison This volume is intended to serve as a didactically-oriented resource covering a broad range of advanced topics often not discussed in introductory courses on structural equation modeling (SEM). Such topics are important in furthering the understanding of foundations and assumptions underlying SEM as well as in exploring SEM as a potential tool to address new types of research questions that might not have arisen during a first course. Chapters focus on the clear explanation and application of topics, rather than on analytical derivations, and contain syntax and partial output files from popular SEM software. CONTENTS: Introduction to Series, Ronald C. Serlin. Preface, Richard G. Lomax. Dedication. Acknowledgements. Introduction, Gregory R. Hancock & Ralph O. Mueller. Part I: Foundations. The Problem of Equivalent Structural Models, Scott L. Hershberger. Formative Measurement and Feedback Loops, Rex B. Kline. Power Analysis in Covariance Structure Modeling, Gregory R. Hancock. Part II: Extensions. Evaluating Between-Group Differences in Latent Variable Means, Marilyn S. Thompson & Samuel B. Green. Using Latent Growth Models to Evaluate Longitudinal Change, Gregory R. Hancock & Frank R. Lawrence. Mean and Covariance Structure Mixture Models, Phill Gagne. Structural Equation Models of Latent Interaction and Quadratic Effects, Herbert W. Marsh, Zhonglin Wen, & Kit-Tai Hau. Part III: Assumptions. Nonnormal and Categorical Data in Structural Equation Modeling, Sara J. Finney & Christine DiStefano. Analyzing Structural Equation Models with Missing Data, Craig K. Enders. Using Multilevel Structural Equation Modeling Techniques with Complex Sample Data, Laura M. Stapleton. The Use of Monte Carlo Studies in Structural Equation Modeling Research, Deborah L. Bandalos. About the Authors.
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| Customer Reviews:
Outstanding intermediate level SEM text November 24, 2007 Tor Neilands 5 out of 5 found this review helpful
This book fills an important niche, a sweet spot in between basic SEM texts such as Rex Kline's helpful beginner's book and more complex, advanced treatments of SEM such as Ken Bollen's classic 1989 Wiley text. Methodologists might argue that the latter text is intermediate rather than advanced, but practitioners and applied users of SEM who are not in the business of creating new methods but instead want to use SEM in a rigorous, productive way on applied analysis problems will find this text to be just the ticket to getting things done using SEM and tackling typical problems such as how to handle missing data and how to calculate power for goodness-of-fit tests and parameter estimates. The editors have done a terrific job in working with the chapter authors to make all chapters accessible with helpful examples and consistent notation and terminology. This book, along with Loehlin's Latent Variable Models, is one I find myself pulling off my bookcase repeatedly to solve applied problems or to learn more about a particular SEM issue (modeling options for complex survey data, mixture modeling) quickly, yet comprehensively. Highly recommended.
Clear and easy to understand July 9, 2008 Rabbit with watch (Far away) 1 out of 1 found this review helpful
Very helpful compilation of various topics under one cover. Dr. Hancock is a great instructor and he is known for using clear language and examples to get his point across. I especially enjoyed his chapter on Latent Growth Modeling--very good introductory text and examples, which will help you grasp the idea of LGM even if you don't know much about SEM. Another useful chapter in the book was on using SEM with data from complex design surveys--nice overview of why complex design surveys need special approach as well as practical advice on what to do about it and what software to use (with sample code).
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